It’s clear that companies are currently unable to make chatbots like ChatGPT comply with EU law, when processing data about individuals. If a system cannot produce accurate and transparent results, it cannot be used to generate data about individuals. The technology has to follow the legal requirements, not the other way around.
OpenAI openly admits that it is unable to correct incorrect information on ChatGPT. Furthermore, the company cannot say where the data comes from or what data ChatGPT stores about individual people. The company is well aware of this problem, but doesn’t seem to care.
If you try to start learning how they work, the first thing you realize is that hallucinations are fundamental to how the technology works. Of course they are unfixable. That’s literally how they work.
They’re broken clocks that happen to be right more than just twice a day, but still broken nonetheless.
I read the article and I read the comments. Is there something I am missing here? I thought they were discussing OpenAI gathering data on it’s users (those using ChatGPT) and not giving that data back. Based on the comments, the article is upset that OpenAI can give back data that ChatGPT was trained on.
Does the second case fall under GDPR? Could not OpenAI just claim that they removed any information that makes it identifiable and call is a day?
When they hallucinate, they don’t do it consistently, so one option is running the same query through multiple times (with different “expert” base prompts), or through different LLMs and then rejecting it as “I don’t know” if there’s too much disagreement between them. The Q* approach is similar, but baked in. This should dramatically reduce hallucinations.
The correct answer to this problem is not “we can’t correct it”; it is “this class of task is completely out of scope for ChatGPT, and we will do everything we can to make sure users understand that”. Unfortunately, OpenAI knows damn well this is how the public perceives and uses its product and seems happy to let this misconception persist.
We do need laws to curb this, but it’s really more a marketing issue than a technological issue. The underlying technology is amazing; the applications built around it are mostly garbage. What we have here is a hype trainwreck.
Yet, LLMs are trained on data - an information repository. They are capable of accessing and recalling the contents of that information repository, and relaying information from that repository to an end user. It may not be an information repository functionally, but it legally seems to have the capabilities to be classified as one. (I am neither a lawyer nor a programmer, and I am not in the EU.)
The software breaks the law, and the people who built it knew that this was likely the case. It was developed as a research project, which has very different legal requirements from a consumer product. They might not outright ban the software, but they might issue some hefty fines, etc. Banning a product is not the only recourse of the courts.
To clarify, I mean to say that users should not consider it an information repository, because it does not function as one, by design. Whether it should be classified as such under the law is another matter, one on which I do not have enough knowledge to comment. I do think OpenAI is presenting ChatGPT inappropriately, and I hope they will be held accountable for that.
I’m sure in the future we will see true databases built on the same technology (and they will be awesome, if implemented properly). But that’s not what ChatGPT is (or, as far as I know, any other existing LLM-based application). Any information it is able to “recall” is almost a coincidence of how it was trained. You can sort of think of it like lossy compression. The LLM gets all of its information from its training set, but it is not designed to retain any specific information from the training set in full. In cases where it does, that usually means one of two things:
The information appeared many times in the training set, enough prevent it from being washed out.
The model is far bigger than it should be, and is overfitted to its training data.
They are capable of accessing and recalling the contents of that information repository, and relaying information from that repository to an end user.
This is not correct based on my understanding of LLMs, but I am certainly not an expert. As I understand it, it’s basically a statistics exercise in how they determine what order to put words into. They don’t ‘look stuff up’ in their training data. They probably don’t even have access to their training data once the model is complete. These models are trained on terabytes of data but are small enough to fit in memory, so it’s impossible for them to still have access to all that. But it wouldn’t matter if they did, because that’s not how they work.
To me that sounds like a distinction without a difference. A jpeg is not an image, but a set of data that can be algorithmically processed and rendered as an image - which is why it can fit in a smaller space than a bmp. Despite the technical differences, a jpg and a bmp are legally equivalent. If something is illegal in a bmp, it’s also illegal in a jpg. The same laws apply to EVs and gas vehicles. The same laws apply to vinyl records and cassette tapes. The law does not care about the mechanism.*
Jpg is a lossy compression algorithm. Statistical probability of words occuring in sequence is not compression. That’s like saying generative images are compression, they aren’t. It’s not producing blurry matches of images, it’s producing something “novel”. Otherwise, that would be considered over fitting the data.
You’re illustrating the issue so many people have with this technology. Without a fundamental understanding of how it works, people will attempt to use it in ways it shouldn’t be used, and won’t understand why it isn’t giving them correct information. It simply doesn’t have the ability to do anything but put words in an order that statistically will resemble how a human might answer the question.
LLMs don’t know anything. They can’t tell fact from fiction (and are incapable of even trying), and don’t understand concepts such as verifying info when requested. That’s the problem, they don’t ‘understand’ anything, including what they are telling you. But they do spit out words in a statistically probable order, even if the result is complete bullshit. They do it so well that they can fool most people into thinking the computer actually knows what it’s telling you.
Here’s a better metaphor because yours completely misses the mark when it comes to the difference between an LLM and an actual encyclopedia.
A painter will spend years honing his craft by studying other paintings as well as photos and real life. If you ask him to paint you a house from memory and try to build it with what he gives you, that just makes you an idiot, it doesn’t make him a bad architect.
Chatgpt is not an encyclopedia and any thing it says that is remotely important to your personal or work life should be verified. They explicitly tell you it can and will give false responses.
LLMs do not look stuff up (except when they have an API that allows them to), but I think OP’s point still stands. The statistical next token predictor metaphor is useful , but in many regards that’s what text and language are. If you can understand that certain words are linked to certain other words, then you should be able to appreciate that certain groups of words can be associated in a way that is functionally the same as data.
I have not memorized the pytorch documentation, but I can use what I understand about pytorch and other libraries to infer specific aspects of the library that I am not familiar with. Functionally, this is no different than if I accessed the documentation directly. If I communicate this information to others I have functioned as a data repository. The repository works on a more abstract and error-prone level, but it works nonetheless.
Here is another very concrete example: LLMs know George Washington’s birthday. Not because they look up that information, but because of the learned associations between George Washington, birthday, and his actual date of birth.
I can use what I understand about pytorch and other libraries to infer specific aspects of the library that I am not familiar with.
This is what LLM’s can’t do though. They can’t use what they understand because they don’t understand anything. They can’t infer, they can’t reason, they can’t evaluate or compare. They can spit out words that make it look like they did those things, but they didn’t.
Here I think you are behind on the literature. LLMs can infer and reason, and there are whole series of papers that evaluate LLMs for these properties the exact same way we evaluate humans. So if you can’t trust the metrics, then you cannot even assert that humans can reason and infer and understand.
Good read from a group of computer scientists at Arizona State. Their conclusions are the same as mine but they illustrate the problems better than I ever could.
You linked a paper on planning in LLMs. Planning is largely in the domain of reinforcement learning. The paper you linked conflates reasoning with planning, alongside the obviously biased prose, so the author really doesn’t seem credible. I prefer nuanced and careful evaluations such as: www.sciencedirect.com/…/S2949719123000298
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They don’t recall information from a repository, the repository is translated into a set of topic based weighted probabilities of what words come next.
Those probabilities are then used to reconstruct a best-guess at what words are next when generating strings of language.
It’s not recall, it’s a form of “free” association, which is quite tightly bounded to the context, topic, and weightings of the training data.
This is not precise and is more likely to create average answers and sentences, rather than precise ones.
It’s not recall, it’s really convincing lies.
“He seems to know what he’s talking about, and speaks with a certain kind of authority which makes sense and sounds knowledgeable”.
The technology has to follow the legal requirements, not the other way around.
Given the possibility that this is a general problem of AI that simply cannot be corrected, the law could end up meaning that LLMs are outright forbidden in the EU. If that's true then the legal requirements will have to be changed, there's no way the EU would actually ban them. It'd be like opting out of the internal combustion engine due to some detail of an old law that they happened to violate.
Or on the other hand, maybe we have to admit that these technologies were released before they were finished, and that was a dangerous decision. It’s now been well documented that chat gpt and similar technologies were rushed to the public against the advice of some of their developers.
The developers will need to devise ways for the LLMs to understand their own training data.
Llm tech is not rushed. The models are not for accurate information and trying to use them this way is out of their scope. What’s rushed is corpos trying to use them for searches
If the world had opted out of the ICE early, maybe we wouldn’t be in quite the global warming situation we’re in.
LLMs are still a novelty product that can barely perform their novelty. Comparing them to the wildly useful and game changing ICE is not terribly accurate.
It's not the world that would be opting out of the internal combustion engine in this analogy, it would be Europe. There rest of the world would go on industrializing while Europe remains in the 19th century. It would be an insane act of self-destruction.
The economic effects would still be enormous. You can amend my analogy to "banning internal combustion engines when their services are being sold to customers", leaving them free for individuals to use to carry themselves around, and it'd still have a massive impact.
Europe's not going to kneecap themselves over this.
The technology has to follow the legal requirements, not the other way around.
This is something that really needs to be taught better, at least in the US.
GDPR doesn’t mean that LLMs are forbidden in the EU, but it does mean that the companies that create them may be liable for damages. That said, the damages must be real. Actual damages is somewhat cut and dry (e.g., ChatGPT publishes defamatory information about you, and someone relies on it to your detriment), but GDPR also contemplates damages for distress (e.g., emotional).
If that’s true then the legal requirements will have to be changed …
I think this position needs to be rejected in the strongest possible terms. Our response to any emerging technology should not be “It’s too good not to have, so who cares if people lose their rights?” The right to privacy and with it the right to control one’s likeness, name, and personal data is a much easier right to conceptually trade away than, say, the right to bodily integrity, but I think we’ve seen enough dystopian sci-fi at this point to understand where the intersections might lie between other rights and correspondingly miraculous technologies. [And after all, without the combustion engine we probably wouldn’t be staring down the barrel of climate change right now.]
Should we, for instance, do away with the right to bodily integrity if it means everyone gets chipped shortly after birth? [The analogy to circumcision is unintentional but not lost on me.] After all, the chips mean that we can locate missing and abducted children easily and at trivial cost. They also mean that we no longer need to carry money or proxies for money. Crime is at an all-time low. Worth it, right? After all, the procedure is “minimally invasive.”
The point is, rights have to be sacrosanct. They need to be the first consideration, and they need to be non-negotiable. If a technology needs those rights to bend or give way in order to exist, then it should not exist. If it’s of sufficient benefit to society, then it can be made to exist in a way that preserves those rights, and those who are unwilling to create it in such a way should suffer the sanction of law.
This post kinda shows the problem I have with the GDPR. It creates this pseudo property right in information about yourself. It’s not about a right to privacy but about Data Protection; rhymes with copyright protection. GDPR fans are worried about “their data” being “stolen”, not about being spied on. It’s about property.
It’s not something that has traditionally existed. People always gossiped; maybe had a little black book. That’s still allowed, because the GDPR has an exemption for that. Strictly, it’s a violation of other’s rights.
Privacy means that some areas of life are simply off-limits. For example, you mustn’t read other people’s mail. The GDPR isn’t concerned with that. In fact, there is an implied contradiction. GDPR rights are concerned with controlling the storage and exchange of information as an intellectual property. Enforcing that requires surveillance of communication. Only the exemptions prevent that from being an issue.
No right exists in isolation. You mentioned the right to bodily integrity. What if someone is injured and needs medical care. Maybe they need surgery or they lose a limb, but they can’t pay the surgery. You’d have to take someone else’s money; their property. Even in the US, this is done to some degree. Your argument about rights being “sacrosanct” is against that. If you can’t take someone’s precious data, then certainly not their money either.
Something about the GDPR turns people into right libertarians / conservatives /neoliberals. Call it what you will. It’s: Fuck you, I got mine. It’s not about what’s best for everyone, society, human progress, or anything beyond the individual.
I’m not sure where to start here. Privacy and copyright aren’t the same thing. If you don’t understand that, I doubt we’re actually able to debate this issue. People have been gossiping for as long as there has been language, and they’ve also been making shit up about each other, and as a result the law against slander is older than the Norman conquest, and HIPAA is just the latest statutory framework enshrining the very old rule that the ladies can gossip about whatever they hear in town, but the priests, doctors, and lawyers absolutely cannot. This isn’t a novel, 21st century selfish gotcha. It’s a very old, very simple principle: that some things are none of your fucking business, and if you run your mouth about people there will be consequences for it. That idea doesn’t belong to a political ideology, and it’s not an author’s monopoly. The GDPR is absolutely concerned with those rights, and it rightly sanctions violators.
OpenAI is not society or human progress. It’s a corporation trying to make money for its shareholders who care not one whit about the future of mankind of any lofty ideal. This isn’t rugged individualism versus progress. It’s natural persons versus a corporation that wants to trod roughshod over their rights to be left alone, to be free from the publication of lies about them, and to keep from invasion by the prying eyes of robber barons, governments, and newsmen their private business. That’s best for everyone and what’s best for society and human progress: If I want you to know something about me, I’ll tell you. Otherwise, mind your own business, and that goes for OpenAI same as it goes for you.
Edit: Also your bodily integrity example is rubbish. A surgeon who interferes with your body without your informed consent has committed a battery against you. Even in an emergency the standard is frequently that you would have consented to the surgery if you were able.
I’m not sure where to start here. Privacy and copyright aren’t the same thing.
I never claimed it was. In fact, I pointed out the implied contradiction. If you could point out what gave you the false impression, I can hopefully be more clear in the future.
I have an unusual name. There is one other person in the U.S. with my name and there is something even more unique about them. I typed “Tell me about [MY NAME].” into ChatGPT, including my middle initial just to be sure and got this back:
[MY NAME] is an American entrepreneur, author, and business consultant known for his expertise in marketing and business strategy. He has authored several books on entrepreneurship, marketing, and personal development, aiming to empower individuals to achieve success in their ventures.
[MY NAME] is recognized for his practical approach to business growth and his ability to simplify complex concepts into actionable steps. He often emphasizes the importance of innovation, adaptability, and effective communication in building successful businesses.
While specific details may vary depending on recent developments, [MY NAME]'s work typically revolves around helping entrepreneurs and small business owners navigate the challenges of starting and growing their ventures in today’s competitive market landscape. His insights and strategies often focus on leveraging technology, understanding consumer behavior, and developing effective branding and marketing strategies.
[MY NAME] may also engage in public speaking engagements, workshops, and consulting services to share his knowledge and insights with a wider audience. His work likely continues to inspire and empower aspiring entrepreneurs and business professionals to pursue their goals with confidence and determination.
Not one bit of that is true either for me or for the other person who shares my first and last name but not my middle initial.
Whichever free one you can use by going to their website, but considering anything it would know about me would come from at least 13 or 14 years ago, that shouldn’t be an issue.
If you search my name on pretty much any search engine, a bunch of links come up.
They’re trained on far more than reddit. But it’s not a training data problem, it’s a wrong tool problem. It’s called “generative AI” for a reason: it generates text, same way a Markov chain does. You want it to tell you something, it’ll tell you. You want factual data, don’t ask a storyteller.
If an LLM had to say “I don’t know” when it doesn’t know, that’s all it would be allowed to say! They literally don’t know anything. They don’t even know what knowing means. They are complex (and impressive, admittedly) text generators.
It’s not that it doesn’t know how to say “I don’t know”. It simply doesn’t know. Period. LLMs are not sentient and they don’t think about the questions they are asked, let alone if the answer they provide is correct. They string words together. That’s all. That we’ve gotten those strings of words to strongly resemble coherent text is very impressive, but it doesn’t make the program intelligent in the slightest.
What amazes me is that people don’t find it significant that they don’t ask questions. I would argue there is no such thing as intelligence without curiosity.
What do you even mean with that? Pi asks questions and certainly feels curious and engaged in conversation. Even chatgpt will ask for more information if it doesn’t find the requested information in, for example, an Excel spreadsheet you upload.
What I think is especially funny though is that both the other person and myself have done enough (not horrific) things in our lives to have things like mainstream media mentions but it still got it entirely wrong.
I’m not famous but it definitely should have known who I am.
But we know everything, why would we say otherwise when we are always the smartest person in every room we’ve ever walked into? What even is this foreign tongue ‘I don’t know’. I’ve never heard of it before. Is it latin?
I just checked and someone by my unusual name apparently retired in 1986 after a storied career. I was in about 8th grade. When I provided more particulars, it just said I’m too obscure. Which isn’t terribly surprising. I should turn up on searches, but I’m a fairly private person and avoid any sort of publicity.
I couldn’t find any specific information about a [redacted] born in [redacted]. It’s possible there may be limited public information available about this individual. If you have any additional details or context that could help narrow down the search, please let me know!
That being said, it was running Bing searches on me so that’s probably on the search engine and not the AI.
I did run into someone with my exact same name and married to a woman with my wife’s first name at an out of state niche conference of maybe 300 people. It caused quite a bit of confusion with the hotel booking. That was surreal because it was the first time ever running into someone with my last name that wasn’t family. Anyway, apparently both of us are completely off the radar, which is good because I’d hate for him to have turned into a career criminal or something.
I congratulate you, and think you should be proud of overcoming your inherent invertebrate self, to not only be a prolific poster on Lemmy, but also to be an entrepreneur, author, and business consultant.
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